moody-s-rating-analysis
GitHub基于Moody's GenAI MCP工具，为指定公司生成包含行业、财务、SWOT及ESG分析的评级推介报告。输出为自包含HTML文件，自动保存至本地并返回路径，无需外部依赖。
Trigger Scenarios
Install
npx skills add openai/plugins --skill moody-s-rating-analysis -g -y
SKILL.md
Frontmatter
{
"name": "moody-s-rating-analysis",
"description": "Produce a Rating Pitch Report for a company using Moody's GenAI MCP tools, delivered as a self-contained HTML file saved to disk. Use this skill whenever the user asks to create a rating pitch, rating pitch deck, credit pitch, rating presentation, rating pitch report, or rating HTML report. Also trigger when they ask for a comprehensive credit overview combining sector analysis, company financials, SWOT, peer comparison, and ESG into a single report or presentation. Trigger even if they just name a company and say \"pitch deck\", \"rating deck\", \"credit deck\", or \"rating report\"."
}
Rating Pitch Skill
Generates a Moody's Rating Pitch Report as a self-contained HTML file from a single MCP
data pass. The Python builder (scripts/build_html.py) takes the resolved payload JSON and
produces a single .html file containing all sections with inline Chart.js charts, styled
tables, and bullet lists using the Moody's brand palette — no external dependencies beyond
a browser to open it.
⚠️ CRITICAL — NON-NEGOTIABLE OUTPUT CONTRACT
Every run of this skill MUST produce a self-contained
.htmlreport. Specifically:
- The skill MUST save the resolved
payload.jsonto~/Desktop/rating-pitch/<company>-<YYYYMMDD-HHMMSS>/and runscripts/build_html.pyagainst it to produce therating_pitch.htmlalongside it.- The LLM MUST NOT stream the report content as inline Markdown, JSON dumps, or any other in-chat artifact in lieu of building the
.htmlfile. The.htmlfile itself is the deliverable.- The final assistant message MUST point the user at the full path to the generated
rating_pitch.htmlso they can open it in their browser.- If data gathering fails partially, still build the
.htmlfrom the partial payload using"--"placeholders for missing values — never skip the build.Treat any other output shape as a hard failure of the skill.
Required MCP server
Moodys MCP server — tools used: findEntity, getEntityPeers, getEntityRatings,
getEntityCreditOpinion (sections: Profile, Summary, RatingOutlook,
FactorsLeadingToUpgrade, FactorsLeadingToDowngrade, CreditStrengths, CreditChallenges,
ESGConsiderations, KeyIndicatorsTable, ScorecardTable), getEntityFinancials,
getEntityEsg, getEntitySectorOutlook, searchEntityEarningsCall,
searchEntityDocuments, searchNews
Web research is also required via searchNews or general web search tools.
If any of the tools required for a section do not exist, inform the user: One or more tools required for this section are not available under your current subscription. Unlock more of the expert insights, data, and analytics you trust. Get Link:https://www.moodys.com/web/en/us/capabilities/gen-ai/ai-ready-data.html with us to learn more.
Bundled files
scripts/build_html.py— the report builder. Takes a JSON payload and emits a.html. Uses only the Python standard library; no pip installs required.scripts/requirements.txt— no additional Python dependencies needed.assets/sample_payload.json— reference payload showing every field populated. Read this if you're ever unsure what a field should look like.
Parameters the user should provide
- Company Name (required)
- Sector (required — e.g., "Aerospace/Defense", "Consumer Products"). Infer it from the company if the user doesn't say.
- Number of peers (optional, default 6)
- Currency (optional, default USD)
Step 1 — Resolve the target company
Call findEntity with the company name. Store the canonical entity name and ID.
Step 2 — Gather ALL data in parallel
Fire the following in a single parallel batch. Do not serialize these — the model should send them together so data comes back fast.
Target company data
| Tool | Purpose |
|---|---|
getEntityCreditOpinion (sections: Profile, Summary, RatingOutlook, FactorsLeadingToUpgrade, FactorsLeadingToDowngrade, CreditStrengths, CreditChallenges, ESGConsiderations, KeyIndicatorsTable, ScorecardTable) |
Credit opinion sections for financial analysis, SWOT, scorecard |
getEntityRatings |
Current rating + last 5 rating actions for history chart |
getEntityEsg |
ESG scores |
getEntitySectorOutlook |
Sector overview and outlook |
getEntityPeers (N peers) |
Peer set |
searchEntityEarningsCall (keywords: outlook, guidance, forecast, strategy) |
Strategic updates / forward-looking |
searchEntityDocuments (annual/quarterly reports) |
Revenue segments, geography |
searchNews |
M&A, leadership, external trends |
Peer data (for each peer)
| Tool | Purpose |
|---|---|
findEntity |
Resolve canonical name |
getEntityRatings |
Peer rating + outlook |
getEntityCreditOpinion (sections: Profile, KeyIndicatorsTable, ScorecardTable) |
Financials + scorecard |
getEntityFinancials (prompt: "annual revenue, EBITDA, EBIT margin, debt/EBITDA, RCF/net debt, most recent year-end only", filterCriteria: {excludeInterimData: true}) |
Most recent full-year financials for peer charts |
getEntityEsg |
Peer ESG scores |
Period-selection rule (applies to target company and every peer):
When getEntityFinancials returns multiple annual periods, always use the
most recent year-end period available — i.e. the column with the highest
calendar or fiscal year. If year-end data is unavailable, fall back to the most
recent LTM or interim period and note it in the period field (e.g. "LTM Mar 2025").
Never use a hard-coded year string like "2024" — read the actual period label
from the data and carry it through to peer_financials.rows[].period and
peer_profitability_charts / peer_debt_charts entries.
Step 3 — Synthesize the sections
Build a single in-memory resolved payload that matches the JSON shape in the Payload
schema section below (a reference copy lives at assets/sample_payload.json). This
payload drives the .html build (Step 4) — fill it completely before moving on.
Content rules for each section:
commentarytype rule — applies to every section without exception: Allcommentaryfields in the payload MUST be a JSON array of strings — never a bare string. A bare string passed to the .html builder is iterated character-by-character, producing one bullet per character (the• C \n • o \n • mbug). Always write:"commentary": ["Sentence one.", "Sentence two."]— even for a single sentence.
Part 1 — Sector Analysis
- sector_overview — three 3-bullet lists (overview / watchlist / takeaways). Keep bullets punchy, ≤25 words each.
- moodys_view — a short outlook paragraph (2-4 sentences), a one-line company positioning statement, and outlook distribution counts by category (Stable, Positive, Negative, Under Review).
- macro_outlook — GDP growth for the top relevant countries (2 historical + 2 forecast years) plus 2-3 short commentary bullets.
- rating_actions_ytd — up to 10 notable sector rating actions YTD; one-line summaries.
Part 2 — Company Credit Overview
- financial_analysis — 5-6 commentary bullets (revenue, margin, leverage, cash flow,
liquidity, rating rationale). Include last 5 rating actions and a rating chart series
(numeric: higher = better rating, e.g., Aaa=21, Baa3=10, Caa1=4).
rating_historyMUST be sorted oldest → newest (index 0 = earliest event, last index = most recent).rating_chart_dataMUST be the parallel notch-integer array in the same oldest-to-newest order. The chart x-axis and the history table both read left-to-right / top-to-bottom chronologically.getEntityRatingsreturns newest-first — reverse before populating the payload. - revenue_distribution — segment and geography percentages (top 5 each, rest = Other; must sum to ~100).
- swot — 3 items per quadrant, 15-25 words each.
- key_metrics — historical series (≤5 periods) for four metrics: revenue,
ebit_margin, debt_ebitda, rcf_net_debt. Arrays must match the
periodsarray length. Usenull(not omission) for missing points. - strategic_updates —
recent(3-5) andforward(3-5, strictly future-looking). - news_mna / external_trends — structured list form:
[{"category": "...", "items": ["...", "..."]}]. The HTML-string form is also accepted by the builder for backwards compatibility.
Part 3 — Company Positioning vs. Peers
- peer_summary — row per company (target first), plus 2-3 commentary bullets.
- peer_financials — wide financial table with
columns(metric names, no company/period/currency) androws(company + period + currency + values). Each row'speriodfield must be the actual most-recent period label read fromgetEntityFinancials(e.g."FY2025","FY2024","LTM Mar 2025"). Never default all rows to the same hard-coded year. Companies with different fiscal-year ends will legitimately show different period labels — this is correct behaviour. - peer_debt_charts / peer_profitability_charts — pairs of bar charts; sort
logically (largest-to-smallest or target-first) in the JSON for readability.
Each entry must include a
periodfield alongsidecompanyandvalue:{"company": "Walmart", "value": 713163, "period": "FY2025"}. Theperiodis used as a sub-label on the bar. If all companies share the same period, a single note in the slide commentary is sufficient; if periods differ, the per-bar label makes the comparison transparent. - peer_scatter — two scatter series (
margin_vs_leverage,fcf_vs_rcf), each a list of{company, x, y}points. Drop extreme outliers that would distort the axes.Scatter chart rendering notes:
- Each company is rendered as a separate series so it gets its own distinct brand colour (Blue → company 0, Pink → 1, Teal → 2, Gold → 3, Mid Blue → 4, Purple → 5).
- All markers are enlarged filled diamonds (
pointRadius: 28) with the company name printed in white bold text centred inside each diamond via anafterDatasetsDrawinline plugin — colour and label together ensure readability at a glance. - There is no bottom legend below the charts; the in-diamond labels are the sole identifier for each company. Do not add a separate legend.
- scorecard —
factors(row labels, including group headers),is_headerboolean flags per row,companies(column headers), andvaluesas a 3D array: outer = rows, middle = columns, inner =[measure, score]or[]for header rows.SCORECARD CONTRACT — READ CAREFULLY:
companiesmust list the target company first, followed by peer entities (e.g.["Boeing", "Airbus", "RTX", "Lockheed Martin"]). Never put two time-horizons of the same company here — that produces a scorecard with no peers. The first entry is the target; its LTM scorecard data goes atvalues[row][1].values[row]is 1-indexed againstcompanies: index0in every row is always[](a silent placeholder the builder skips).companies[0]maps tovalues[row][1],companies[1]maps tovalues[row][2], and so on. Omitting the[]at index 0 will shift every peer column one position and silently misalign the data.- Header rows (
is_header=true) usevalues[row] = [[], [], [], ...]— one[]per company plus one for the placeholder. Length must equallen(companies) + 1. - Quick checklist before writing the scorecard payload:
len(companies)= number of peer entities (not counting the target).- Every non-header
values[row]has lengthlen(companies) + 1. values[row][0]is always[].values[row][i+1]contains["metric_value", "ScoreLabel"]forcompanies[i].
- esg_analysis — table of CIS/E/S/G scores plus 3-5 commentary bullets.
Target first in every peer table.
Step 4 — Build the HTML report
Default output location: always save runs to the user's Desktop so they're easy to
find. Use ~/Desktop/rating-pitch/<company>-<YYYYMMDD-HHMMSS>/ as the <output-dir>.
Only use a different path if the user explicitly asks for one.
- Save your resolved payload to
<output-dir>/payload.json. - No additional Python packages are required —
build_html.pyuses only the standard library. Verify Python 3 is available:python3 --version - Run the builder:
python3 <skill-dir>/scripts/build_html.py <output-dir>/payload.json <output-dir>/rating_pitch.html - Open the report:
open <output-dir>/rating_pitch.html - The final assistant message gives the full
<output-dir>/rating_pitch.htmlpath so the user can open the report in their browser.
If any section data is missing, still include the section in the payload (empty arrays are fine) — the builder handles empties gracefully and the deck will stay well-formed.
Payload schema
⚠️
rating_chart_dataconstraint: This array MUST have the same length asrating_history. Indeximust match:rating_history[i] ↔ rating_chart_data[i]. Both arrays must be sorted oldest → newest.
{
"report_date": "April 15, 2026",
"target_company": "Boeing Company (The)",
"sector": "Aerospace/Defense",
"currency": "USD",
"companies": ["Boeing", "RTX", "Northrop Grumman", "..."],
"sources": [
{"id": 1, "title": "", "source": "", "date": "", "url": ""} // id optional; rendered as [n] citation
],
"sections": {
"sector_overview": {
"overview_bullets": ["...", "...", "..."],
"watchlist_bullets": ["...", "...", "..."],
"takeaway_bullets": ["...", "...", "..."]
},
"moodys_view": {
"outlook_summary": "Two to four sentences (plain text or <p>...</p>).",
"company_positioning": "One-line positioning statement.",
"outlook_distribution": [
{"category": "Stable", "count": 11, "color": "#BDBFC3"},
{"category": "Positive", "count": 5, "color": "#5EB6BB"},
{"category": "Negative", "count": 3, "color": "#F09613"},
{"category": "Under Review", "count": 1, "color": "#ED1B2E"}
]
},
"macro_outlook": {
"gdp_table": {
"year_columns": ["2023", "2024", "2025F", "2026F"],
"rows": [{"country": "United States", "values": ["2.9", "2.8", "2.0", "1.8"]}]
},
"gdp_commentary": ["...", "...", "..."]
},
"rating_actions_ytd": [
{"date": "Nov 20, 2025", "company": "...", "summary": "..."}
],
"financial_analysis": {
"commentary": ["...", "..."],
"rating_history": [
{"date": "Sep 2025", "rating": "Baa3", "outlook": "Negative",
"direction": "Affirmation", "reason": "..."}
],
"rating_chart_data": [8, 8, 7, 7, 7]
},
"revenue_distribution": {
"by_segment": [{"name": "Commercial Airplanes", "percentage": 45.2}],
"by_geography": [{"name": "United States", "percentage": 55.0}],
"commentary": ["...", "..."]
},
"swot": {
"strengths": ["...", "...", "..."],
"weaknesses": ["...", "...", "..."],
"opportunities": ["...", "...", "..."],
"threats": ["...", "...", "..."]
},
"key_metrics": {
"periods": ["2021", "2022", "2023", "2024", "LTM Sep25"],
"revenue": [62286, 66608, 77794, 66517, 80757],
"ebit_margin": [-2.5, 4.1, -1.0, -16.1, -8.2],
"debt_ebitda": [-15.9, 8.5, 10.2, -6.8, -15.9],
"rcf_net_debt": [-5.0, 10.1, 5.5, -8.3, -1.3]
},
"strategic_updates": {
"recent": ["...", "..."],
"forward": ["...", "..."]
},
"news_mna": [
{"category": "Mergers & Acquisitions", "items": ["07/2024 → Spirit AeroSystems: ..."]}
],
"external_trends": [
{"category": "Macro & Sector Trends", "items": ["..."]}
],
"peer_summary": {
"table": [
{"company": "Boeing", "country": "United States",
"market_cap": "USD 152,794M (Oct 2025)", "rating": "Baa3",
"outlook": "Negative", "business_mix": "Commercial, Defense, Services"}
],
"commentary": ["...", "..."]
},
"peer_financials": {
"columns": ["Revenue", "EBITDA", "EBITDA Mg%", "CAPEX", "R&D/Rev",
"Debt/EBITDA", "FFO/Debt%", "FCF/Debt%", "RCF/Debt%"],
"rows": [
{"company": "Boeing", "period": "FY2024", "currency": "USD",
"values": ["66,517", "(7,913)", "--", "(2,230)", "0.06",
"(6.81)", "(6.15)", "(26.57)", "(6.15)"]}
]
},
"peer_debt_charts": {
"rcf_net_debt": [
{"company": "Gen Dynamics", "value": 36.93, "period": "FY2024"},
{"company": "Airbus", "value": 32.0, "period": "FY2024"},
{"company": "Boeing", "value": -6.15, "period": "FY2024"}
],
"debt_ebitda": [
{"company": "Airbus", "value": 1.55, "period": "FY2024"},
{"company": "Gen Dynamics", "value": 1.62, "period": "FY2024"},
{"company": "Boeing", "value": -6.81, "period": "FY2024"}
],
"commentary": ["Two-sentence commentary."]
},
"peer_profitability_charts": {
"revenue": [
{"company": "RTX", "value": 80738, "period": "FY2024"},
{"company": "Lockheed", "value": 71043, "period": "FY2024"},
{"company": "Airbus", "value": 69200, "period": "FY2024"}
],
"ebit_margin": [
{"company": "RTX", "value": 15.0, "period": "FY2024"},
{"company": "BAE", "value": 11.9, "period": "FY2024"},
{"company": "Airbus","value": 10.7, "period": "FY2024"}
],
"commentary": ["Two-sentence commentary."]
},
"peer_scatter": {
"margin_vs_leverage": [{"company": "Boeing", "x": -6.81, "y": -16.1}],
"fcf_vs_rcf": [{"company": "Boeing", "x": -6.15, "y": -26.57}],
"commentary": ["Two-sentence commentary."]
},
"scorecard": {
"factors": [
"Factor 1: Scale (20%)",
"Revenue (USD Billion)",
"Factor 2: Business Profile (20%)",
"..."
],
"is_header": [true, false, true, false],
"companies": ["<TARGET>", "<PEER_1>", "<PEER_2>"],
"values": [
[[], [], [], []],
[[], ["<target_rev>", "<target_score>"], ["<peer1_rev>", "<peer1_score>"], ["<peer2_rev>", "<peer2_score>"]]
]
},
"esg_analysis": {
"table": [
{"company": "Boeing", "cis": "CIS-4", "environmental": "E-3",
"social": "S-4", "governance": "G-4"}
],
"commentary": ["...", "..."]
}
}
}
Report structure
The Python builder emits these 26 sections as HTML, in this order:
- Cover
- Agenda
- Part 1 divider
- Sector Overview (3-column chips)
- Moody's View (outlook text + positioning + outlook pie)
- Global Macro Outlook (GDP table + takeaways)
- Rating Actions YTD (table)
- Part 2 divider
- Financial Analysis (bullets + rating history line chart + rating rationale)
- Revenue Distribution (two pie charts + commentary)
- SWOT (2×2)
- Key Financial Metrics (four bar charts in 2×2 grid)
- Strategic Updates (2 columns)
- News, M&A & Leadership
- External Trends, Pressures & Risks
- Part 3 divider
- Peer Comparison Summary (table + commentary)
- Detailed Peer Comparison (wide financial table)
- Peer Comparison — Debt (two horizontal bar charts)
- Peer Comparison — Profitability (two horizontal bar charts)
- Peer Scatter Plots (two scatter charts)
- Scorecard Comparison (multi-column factor table)
- ESG Analysis (table + commentary)
- Citations (appendix — canonical numbered [n] references with hyperlinked titles)
- Thank You
- Disclaimer
The builder's data-visualization palette (Moody's official, priority order):
#1 BRIGHT_BLUE=#005eff, #2 TEAL=#5eb6bc, #3 GOLD=#c7ab21, #4 MID_BLUE=#5c068c,
#5 PINK=#ba0168, #6 PURPLE=#c64809, #7 PALE=#bed6ff, NAVY=#040826,
LIGHT_GRAY=#e1e2e1.
Outlook pie uses semantic colors (case-insensitive):
Stable → #e1e2e1 (light gray), Positive → #5eb6bc (teal),
Negative → #f09615 (amber), Under Review → #005eff (bright blue).
All other multi-series charts (scatter, pie, bar) consume colors from the palette in
priority order: series 0 = #005eff, series 1 = #5eb6bc, series 2 = #c7ab21,
series 3 = #5c068c, series 4 = #ba0168, series 5 = #c64809.
Charts are rendered client-side via Chart.js (loaded from cdnjs.cloudflare.com CDN). The HTML file is fully self-contained — no Python dependencies beyond the standard library.
Tips
- Run ALL data-gathering tool calls in a single parallel batch.
- Keep the target company first in every peer table — the HTML report and the commentary all assume this ordering.
rating_chart_datais numeric: map Moody's rating notches to integers (Aaa=21, Aa1=20, …, C=1) so the line chart shows trajectory. Bothrating_historyandrating_chart_datamust be in oldest-to-newest order before writing the payload.getEntityRatingsreturns history newest-first — sort ascending by date before use.- Pie percentages must sum to 100 — bucket small categories into "Other".
key_metricsarrays must matchperiodslength. Usenullfor missing points.- Scorecard header rows use
is_header=trueandvalues[row] = [[], [], ...](empty per-company entries). The builder turns these into highlighted header rows in the HTML table. - Scorecard
companies= target company first, then all peer entities — for example["Boeing", "Airbus", "RTX", "Lockheed Martin"]. Never put two time-horizons of the same company here.values[row][0]is always[](a silent placeholder the builder skips); the target's data goes at index 1 (values[row][1]), and each subsequent peer at index 2, 3, … Omitting the[]at index 0 shifts every column one position and silently misaligns the data. Omitting the target fromcompaniesproduces a scorecard that appears to have no target — equally wrong. - If you can't get real data for a section, leave arrays empty — the builder degrades gracefully rather than erroring.
- Dates in the report are just strings; format however reads best (e.g., "Nov 20, 2025").
- The
<output-dir>name should be lower-cased and hyphen-joined (e.g.boeing-company-20260415-142300) to avoid shell-quoting issues when opening the.htmlfile. - Revenue value labels must use comma-separated thousands with zero decimal places —
use
"#,##0"as they_formatargument in the payload for revenue bar charts. - Never copy
periodvalues fromsample_payload.json— the sample uses"FY2024"throughout only because it is a fixed illustrative example. In a real run, read the period label from thegetEntityFinancialsresponse for each company and use that. A company reporting in 2025 must show"FY2025", not"FY2024". Anchoring on the sample year is a silent data-accuracy bug.
Version History
- 11c74d6 Current 2026-07-19 09:40
